Bayess Theorem

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The Theory That Would Not Die

Author : Sharon Bertsch McGrayne
Publisher : Yale University Press
ISBN 13 : 0300175094
Total Pages : 336 pages
Book Rating : 4.3/5 (1 download)

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Book Synopsis The Theory That Would Not Die by : Sharon Bertsch McGrayne

Download or read book The Theory That Would Not Die written by Sharon Bertsch McGrayne and published by Yale University Press. This book was released on 2011-05-17 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This account of how a once reviled theory, Baye’s rule, came to underpin modern life is both approachable and engrossing" (Sunday Times). A New York Times Book Review Editors’ Choice Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the generations-long human drama surrounding it. McGrayne traces the rule’s discovery by an 18th century amateur mathematician through its development by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—while practitioners relied on it to solve crises involving great uncertainty and scanty information, such as Alan Turing's work breaking Germany's Enigma code during World War II. McGrayne also explains how the advent of computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security. Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

Think Bayes

Author : Allen Downey
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491945443
Total Pages : 213 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Think Bayes by : Allen Downey

Download or read book Think Bayes written by Allen Downey and published by "O'Reilly Media, Inc.". This book was released on 2013-09-12 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you know how to program with Python, and know a little about probability, you're ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math to help you learn Bayesian fundamentals. Once you get the math out of the way, you'll be able to apply these techniques to real-world problems.

Bayes' Rule

Author : James V. Stone
Publisher : Sebtel Press
ISBN 13 : 0956372848
Total Pages : 184 pages
Book Rating : 4.9/5 (563 download)

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Book Synopsis Bayes' Rule by : James V. Stone

Download or read book Bayes' Rule written by James V. Stone and published by Sebtel Press. This book was released on 2013-06-01 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this richly illustrated book, the tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for the novice who wishes to become familiar with the basic principles of Bayesian analysis.

Bayes' Theorem Examples

Author : Dan Morris
Publisher : Independently Published
ISBN 13 : 9781549761744
Total Pages : 112 pages
Book Rating : 4.7/5 (617 download)

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Book Synopsis Bayes' Theorem Examples by : Dan Morris

Download or read book Bayes' Theorem Examples written by Dan Morris and published by Independently Published. This book was released on 2016-10-02 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples "...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem..." - Doug E. - TOP 100 REVIEWER"...For those who are predominately "Visual Learners", as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact." - Tin F. TOP 50 REVIEWER

Bayesian Probability for Babies

Author : Chris Ferrie
Publisher : Sourcebooks, Inc.
ISBN 13 : 1728213517
Total Pages : 26 pages
Book Rating : 4.7/5 (282 download)

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Book Synopsis Bayesian Probability for Babies by : Chris Ferrie

Download or read book Bayesian Probability for Babies written by Chris Ferrie and published by Sourcebooks, Inc.. This book was released on 2019-07-02 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fans of Chris Ferrie's Rocket Science for Babies, Astrophysics for Babies, and 8 Little Planets will love this introduction to the basic principles of probability for babies and toddlers! Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child's mind. If you took a bite out of a cookie and that bite has no candy in it, what is the probability that bite came from a candy cookie or a cookie with no candy? You and baby will find out the probability and discover it through different types of distribution. Yet another Baby University board book full of simple explanations of complex ideas written by an expert for your future genius! If you're looking for baby math books, probability for kids, or more Baby University board books to surprise your little one, look no further! Bayesian Probability for Babies offers fun early learning for your little scientist!

Bayesian Statistics the Fun Way

Author : Will Kurt
Publisher : No Starch Press
ISBN 13 : 1593279566
Total Pages : 258 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis Bayesian Statistics the Fun Way by : Will Kurt

Download or read book Bayesian Statistics the Fun Way written by Will Kurt and published by No Starch Press. This book was released on 2019-07-09 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

Proving History

Author : Richard C. Carrier
Publisher : Prometheus Books
ISBN 13 : 1616145609
Total Pages : 361 pages
Book Rating : 4.6/5 (161 download)

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Book Synopsis Proving History by : Richard C. Carrier

Download or read book Proving History written by Richard C. Carrier and published by Prometheus Books. This book was released on 2012-04-03 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This in-depth discussion of New Testament scholarship and the challenges of history as a whole proposes Bayes’s Theorem, which deals with probabilities under conditions of uncertainty, as a solution to the problem of establishing reliable historical criteria. The author demonstrates that valid historical methods—not only in the study of Christian origins but in any historical study—can be described by, and reduced to, the logic of Bayes’s Theorem. Conversely, he argues that any method that cannot be reduced to this theorem is invalid and should be abandoned. Writing with thoroughness and clarity, the author explains Bayes’s Theorem in terms that are easily understandable to professional historians and laypeople alike, employing nothing more than well-known primary school math. He then explores precisely how the theorem can be applied to history and addresses numerous challenges to and criticisms of its use in testing or justifying the conclusions that historians make about the important persons and events of the past. The traditional and established methods of historians are analyzed using the theorem, as well as all the major "historicity criteria" employed in the latest quest to establish the historicity of Jesus. The author demonstrates not only the deficiencies of these approaches but also ways to rehabilitate them using Bayes’s Theorem. Anyone with an interest in historical methods, how historical knowledge can be justified, new applications of Bayes’s Theorem, or the study of the historical Jesus will find this book to be essential reading.

Rational Descriptions, Decisions and Designs

Author : Myron Tribus
Publisher : Elsevier
ISBN 13 : 1483146235
Total Pages : 500 pages
Book Rating : 4.4/5 (831 download)

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Book Synopsis Rational Descriptions, Decisions and Designs by : Myron Tribus

Download or read book Rational Descriptions, Decisions and Designs written by Myron Tribus and published by Elsevier. This book was released on 2013-10-22 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rational Descriptions, Decisions and Designs is a reference for understanding the aspects of rational decision theory in terms of the basic formalism of information theory. The text provides ways to achieve correct engineering design decisions. The book starts with an understanding for the need to apply rationality, as opposed to uncertainty, in design decision making. Inductive logic in computers is explained where the design of the machine and the accompanying software are considered. The text then explains the functional equations and the problems of arriving at a rational description through some mathematical preliminaries. Bayes' equation and rational inference as tools for adjusting probabilities when something new is encountered in earlier probability distributions are explained. The book presents as well a case study concerning the error made in following specifications of spark plugs. The author also explains the Bernoulli trials, where a probability that a better hypothesis than that already adopted may exist. The rational measure of uncertainty and the principle of maximum entropy with sample calculations are included in the text. After considering the probabilities, the decision theory is taken up where engineering design follows. Examples regarding transmitter and voltmeter designs are presented. The book ends by explaining probabilities of success and failure as applied to reliability engineering, that it is a state of knowledge rather than the state of a thing. The text can serve as a textbook for students in technology engineering and design, and as a useful reference for mathematicians, statisticians, and fabrication engineers.

Bayes Rules!

Author : Alicia A. Johnson
Publisher : CRC Press
ISBN 13 : 1000529568
Total Pages : 606 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Bayes Rules! by : Alicia A. Johnson

Download or read book Bayes Rules! written by Alicia A. Johnson and published by CRC Press. This book was released on 2022-03-03 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Bayes’ Theorem and Bayesian Statistics

Author : Lee Baker
Publisher : Lee Baker
ISBN 13 :
Total Pages : 30 pages
Book Rating : 4./5 ( download)

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Book Synopsis Bayes’ Theorem and Bayesian Statistics by : Lee Baker

Download or read book Bayes’ Theorem and Bayesian Statistics written by Lee Baker and published by Lee Baker. This book was released on with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayes’ Theorem is hard. Is it, though? If you flick through any of the other books on Bayesian statistics you’ll get the distinct impression that you’ll have a lot of really hard maths to do, and it can be really intimidating. But is that what Bayesian stats is really all about? If you’re wondering whether you should have a look at Bayesian statistics to see if it’s right for you, then Bayes’ Theorem and Bayesian Statistics in the Getting Started With Statistics series is your first port of call. If what you need is a short guide to getting started, a snappy little non-threatening introduction to Bayes’ Theorem and Bayesian Statistics that dispels the biggest myths, answers the most frequently asked questions and inspires you to take the next steps in your journey, then look no further. Bayes’ Theorem and Bayesian Statistics is that guide. This book is not written for statisticians. Nor is it written by a statistician. A Physicist by trade, and a self-taught statistician, I may have worked (and taught) as a statistician for several years but I have my own struggles with statistics, so I understand where the hard bits are. Better still, I know how to explain them to others in plain English without using difficult to understand technical terminology. That’s what you can expect in this book. First, I’ll explain what Bayes’ Theorem is in simple terms. Then you’ll move on to understanding what conditional probability is and why you don’t need it if you want to find a parking spot, but you do if you’re playing cards (and you want to win). You’ll learn about Prior and Posterior probabilities, and use them to work out if you need to take a brolly to the beach with you (spoiler alert – I live in Scotland. I always need to take a brolly to the beach!). Then I’ll bust a few myths about what Bayesian statistics is – and what it isn’t. By this point you’ll have made up your mind about whether you want to go further, so I’ll show you how to take your next steps. Bayes’ Theorem and Bayesian Statistics makes no assumptions about your previous experience and is perfect for beginners and the Bayes-curious! Discover the world of Bayes’ Theorem and Bayesian Statistics. Get this book, TODAY!

Data Science Algorithms in a Week

Author : Dávid Natingga
Publisher : Packt Publishing Ltd
ISBN 13 : 178980096X
Total Pages : 207 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Data Science Algorithms in a Week by : Dávid Natingga

Download or read book Data Science Algorithms in a Week written by Dávid Natingga and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set

Probability and Bayesian Modeling

Author : Jim Albert
Publisher : CRC Press
ISBN 13 : 1351030132
Total Pages : 553 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Probability and Bayesian Modeling by : Jim Albert

Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

The Equation of Knowledge

Author : Lê Nguyên Hoang
Publisher : CRC Press
ISBN 13 : 1000063232
Total Pages : 461 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis The Equation of Knowledge by : Lê Nguyên Hoang

Download or read book The Equation of Knowledge written by Lê Nguyên Hoang and published by CRC Press. This book was released on 2020-06-18 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science introduces readers to the Bayesian approach to science: teasing out the link between probability and knowledge. The author strives to make this book accessible to a very broad audience, suitable for professionals, students, and academics, as well as the enthusiastic amateur scientist/mathematician. This book also shows how Bayesianism sheds new light on nearly all areas of knowledge, from philosophy to mathematics, science and engineering, but also law, politics and everyday decision-making. Bayesian thinking is an important topic for research, which has seen dramatic progress in the recent years, and has a significant role to play in the understanding and development of AI and Machine Learning, among many other things. This book seeks to act as a tool for proselytising the benefits and limits of Bayesianism to a wider public. Features Presents the Bayesian approach as a unifying scientific method for a wide range of topics Suitable for a broad audience, including professionals, students, and academics Provides a more accessible, philosophical introduction to the subject that is offered elsewhere

Bayesian Brain

Author : Kenji Doya
Publisher : MIT Press
ISBN 13 : 026204238X
Total Pages : 341 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Bayesian Brain by : Kenji Doya

Download or read book Bayesian Brain written by Kenji Doya and published by MIT Press. This book was released on 2007 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Bayes's Theorem

Author : Richard Swinburne
Publisher : OUP/British Academy
ISBN 13 : 9780197263419
Total Pages : 160 pages
Book Rating : 4.2/5 (634 download)

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Book Synopsis Bayes's Theorem by : Richard Swinburne

Download or read book Bayes's Theorem written by Richard Swinburne and published by OUP/British Academy. This book was released on 2005-05-12 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayes's theorem is a tool for assessing how probable evidence makes some hypothesis. The papers in this volume consider the worth and applicability of the theorem. Richard Swinburne sets out the philosophical issues. Elliott Sober argues that there are other criteria for assessing hypotheses. Colin Howson, Philip Dawid and John Earman consider how the theorem can be used in statistical science, in weighing evidence in criminal trials, and in assessing evidence for the occurrence of miracles. David Miller argues for the worth of the probability calculus as a tool for measuring propensities in nature rather than the strength of evidence. The volume ends with the original paper containing the theorem, presented to the Royal Society in 1763.

Bayesian Methods for Hackers

Author : Cameron Davidson-Pilon
Publisher : Addison-Wesley Professional
ISBN 13 : 0133902927
Total Pages : 551 pages
Book Rating : 4.1/5 (339 download)

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Book Synopsis Bayesian Methods for Hackers by : Cameron Davidson-Pilon

Download or read book Bayesian Methods for Hackers written by Cameron Davidson-Pilon and published by Addison-Wesley Professional. This book was released on 2015-09-30 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

Think Bayes

Author : Allen B. Downey
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492089435
Total Pages : 338 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Think Bayes by : Allen B. Downey

Download or read book Think Bayes written by Allen B. Downey and published by "O'Reilly Media, Inc.". This book was released on 2021-05-18 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems